How to Enhance Detection with Comparative Case Analysis

Posted on September 30, 2025 by Paul Curwell

Paul Curwell

5–7 minutes

3 Key Takeaways

Comparative Case Analysis (CCA) isn’t just theory — it’s a practical method to connect the dots between trade secrets theft, fraud, insider threats, and supply chain abuse.
You don’t need a huge internal dataset — competitor incidents and cross-industry cases provide the patterns and behaviours you need to build robust typologies.
CCA creates tangible business value — done properly, it turns messy case data into insights that protect revenue, IP, and operational continuity, making you look good to management and investors.

What is Comparative Case Analysis?

Most companies already have clues sitting in plain sight — case files, legal documents, media reports, competitor incidents, industry analyses. But they rarely connect the dots. If you don’t connect the dots, you can’t detect threats early, which means losses escalate, your IP gets compromised, and supply chain integrity suffers before anyone even notices.

Comparative Case Analysis (CCA) fixes this. It might not show up in glamorous keynote speeches, but it gives you practical leverage: more accurate detection, fewer false alarms, and stronger business protection. If revenue protection, IP protection, and supply chain integrity matter to you (spoiler: they should), then this is your toolkit.

Comparative Case Analysis means taking several instances of risk events (fraud, IP theft, insider threat, etc.), comparing them systematically, extracting patterns, signatures, and behaviours, then using those insights to write typologies which are used to build detection mechanisms. It’s the bridge between one-off incidents and repeatable defence.

Even if your organisation is small, you can pull from competitors or other industries — because threats are surprisingly consistent.

Why Comparative Case Analysis Matters for Business

When you get CCA right, two big things happen:

Earlier detection – You start recognizing threats before they inflict material damage.
Higher accuracy & efficiency – You reduce false positives and false negatives, which means fewer wasted resources and more trust in your detection systems.

That opens the door to greater automation and AI usage. If you understand which threats matter and how they appear in your data, you can lean more on rules engines, models, or anomaly detection — meaning you don’t need huge analyst teams fire‑fighting all day.

The business value isn’t theoretical: avoided losses, protected IP, preserved revenue, fewer disruptions in your supply chain. Plus, when management or investors ask, you’ll have solid proof you’re not just “winging it.”

We often overlook criminology when combating insider threats, fraud and sabotage

The Comparative Case Analysis Value Chain

Here’s the refined flow I use (and teach):

Threats → Risk Events (cases) → CCA (comparison) → Typologies (including patterns, signatures, behaviours) → Detection = Business Value

If any link is weak, the value drops. If all are strong, you build a resilient, measurable defence.

How to Actually Do It (Step‑by‑Step)

Here’s the practical method I use. If you follow this, CCA becomes repeatable, grounded, and useful:

Define your scope
Decide which type(s) of threats matter most to you: IP theft, insider risk, supply chain fraud, etc. Also decide down to the industry, product, or technology level.
Collect cases
Pull from internal cases (incidents, near misses), competitor incidents, public legal filings, academia, and media. If you don’t have five useful internal examples, don’t worry — competitor- or cross‑industry cases are totally valid.
Standardise the data
For each case, capture things like: who, what, when, how, impact, what failed controls, what signatures/behaviours were present.
Compare systematically
Lay out your cases side by side. Look for recurring behaviours, misused access, insider‑outsider collusion, process failures. Don’t assume everything is causal — test what appears consistently.
Extract typologies
From those recurring behaviours/patterns, build your typologies: the defined set of patterns, signatures and behaviours that will become your detection requirements.
Validate & test
Apply typologies to fresh data or unseen cases. Measure whether you catch real threats and don’t swamp people with false positives. Refine aggressively.
Monitor performance
Track detection speed, false positives/negatives, cost of investigation vs. savings, and measurable risk reduction. If you’re not seeing clear value, revisit your typologies.
Peer review
Get someone not involved in your collection or initial comparison to critique: did you miss patterns? Are your assumptions reasonable?
Evaluate reliability
Are your detection rules trustworthy enough to rely on with minimal oversight? If not, iterate.
Refresh regularly
Threats evolve. You should revisit your typologies and the chain every year (or more often in fast‑moving tech sectors) to stay relevant.

“Typologies” Sound Boring – But They Could Save Your Business Millions

Real Case Examples to Learn From

Comparative Case Analysis might not win design awards, but it wins business protection. It turns messy case files into sharp detection requirements. Do it right, and you get fewer losses, protected IP, stable revenue, and less headache from the security/fraud team. For example:

Trade Secret Theft in Medtech: A departing engineer at a medical device company copied proprietary 3D printing designs for a new implant. The designs appeared at a competitor two months later. Compare the methods used to extract the IP, the timing, and which controls failed — then ask yourself: could this happen in your organisation?
Supply Chain Fraud in Electronics: Danish authorities recently discovered unlisted components in circuit boards purchased from overseas, intended for use in green energy infrastructure. The parts could have been exploited to sabotage operations in the future. Compare the tactics and controls in place — quality checks, supplier audits, component verification — and assess whether your supply chain could be similarly vulnerable.
Insider Threat in Critical Infrastructure: A disgruntled employee at a water utility sabotaged Operational Technology at pumping stations so they would fail five days after he left the business. Compare the patterns and tactics used, as well as which controls worked or failed. Then use this to assess your own business: could this happen to you?

These examples demonstrate that threats are not isolated incidents but part of broader patterns that can be identified and mitigated through CCA.

Call to Action

If you’re a risk or compliance leader whose business is exposed to these sorts of threats, you need to ask whether your team is conducting Comparative Case Analysis as part of continuous improvement. Are you systematically comparing incidents to identify patterns? Are you using these insights to write typologies that inform your detection mechanisms? If not, it’s time to start.

The $25 Billion Question: How Much Are You Losing to Warranty Fraud?

Posted on September 9, 2025 by Paul Curwell

Paul Curwell

6–8 minutes

3 Key Takeaways

Warranty fraud is revenue leakage in disguise — costing manufacturers up to $25 billion a year and eating into reserves you thought were safe.
It’s not just customers gaming the system — insiders, dealers, and service providers are often behind the biggest schemes.
You can fight back — with the right contracts, transaction controls, analytics, and service network oversight, you can plug the leaks.

Introduction

A few weeks ago, I wrote about how medtech companies are bleeding millions to revenue leakage in their supply chains. Warranty fraud is another part of that same story — a silent killer of margins that rarely makes it to the executive risk register.

Here’s the uncomfortable truth: the best available global estimates of warranty fraud losses come from studies conducted between 2009 and 2015. That’s right, we’re still relying on decade-old numbers because the industry hasn’t invested in updating them. But the losses — then pegged at around 3% to 10% of total warranty expenses, or roughly $25 billion annually — haven’t magically gone away. If anything, the growth of digital service networks and globalised supply chains has probably made the problem worse.

Executives don’t need another abstract fraud risk to worry about. You need to know how this eats into your bottom line, distorts your financial planning, and ultimately undermines your ability to commercialise new technology. So let’s get practical.

MedTech Companies Are Losing Millions to Revenue Leakage Without Knowing It

The Cost of Warranty Fraud

Warranty fraud is not a rounding error — it’s a profit killer. Surveys by AGMA Global and PwC suggest that warranty and service abuse lead to 3% to 5% revenue losses for manufacturers.

In the U.S. alone, dealer and service provider fraud cost about $2.6 billion in 2018.
Automotive and electronics manufacturers typically spend 2.5% to 2.7% of product revenue on warranty claims. A chunk of that is pure fraud.
Some industries report warranty fraud accounting for up to 15% of total warranty costs.

That’s money straight out of your cash flow. And because fraudulent claims push warranty expenses beyond accrued reserves, the impact doesn’t just hurt margins — it hits your balance sheet, profitability, and valuation.

If you’re courting investors or pushing for commercialisation, warranty fraud doesn’t just look like sloppy operations. It looks like you don’t have control of your supply chain or insider threat risks.

man soldering a circuit board in an electronics warranty service centre — Photo by Quang Nguyen Vinh on Pexels.com

How Fraud Affects Manufacturer Warranty Claim Forecasts

Most manufacturers do their homework when it comes to warranty reserves. Forecasts are based on historical failure rates, reliability data, and statistical modelling. On average:

Companies set aside around 1.4% of product sales revenue to cover warranty claims.
Costs range anywhere from 0.5% to 5%, depending on industry and product complexity.
Automotive and electronics firms typically accrue closer to 2.5% of sales.

This would all work fine — if the claims data reflected reality. Fraud blows a hole in that logic. Fictitious or inflated claims distort the numbers, meaning your forecasts are wrong, your reserves are short, and your cash flow suffers.

For executives, that means warranty fraud is not just a line-item expense. It’s a forecasting and planning risk — the kind of risk that makes boards twitchy and investors cautious. So lets take a look at how it happens.

How Does Warranty Fraud Occur?

Here’s where it gets messy. Warranty fraud is not one type of scam, it’s a whole ecosystem. And unlike other types of fraud, the biggest offenders often sit inside your own supply chain or service networks.

A. Customer Fraud

False claims for non-existent failures.
Misuse or deliberate damage disguised as product defects.
Counterfeit receipts or altered purchase details.

Case Study A – Automotive OEM Dealer Fraud:
Dealers manipulated mileage reporting and double-charged customers and manufacturers for the same repairs. Fraudulent warranty claims stacked up until analytics flagged the anomalies.

B. Dealer and Service Agent Fraud (Insider Threats)

Charging both the customer and the manufacturer for the same repair (classic double-dipping).
Manipulating mileage or usage data to extend warranty coverage.
Repeatedly claiming for the same “repair” months later.

Case Study B – Computer Technology Provider:
One company estimated losing $100 million annually to warranty fraud by service providers. After implementing data-driven fraud detection, they uncovered $11 million in fraud in nine months and saved $67 million over five years.

C. EmployeeS (Insider Threats)

Approving false claims for friends, family, or colluding dealers.
Tampering with data to inflate invoices.
Steering warranty work to preferred suppliers for kickbacks.

Case Study C – Electronics Manufacturer in Australia:
Fined for misleading consumers about their warranty rights — proof that warranty issues don’t just hurt margins, they also create reputational and legal risk.

D. Warranty Provider and Administrator Fraud

Overselling coverage or denying valid claims.
Colluding with dealers or service providers to share the spoils.

Case Study D – Multi-national Manufacturer’s Pan-European Warranty Fraud Detection:
A multi-national manufacturer’s after-sales assessment revealed inconsistent warranty costs across countries attributable to service agent fraud. Fraud detection analytics quickly uncovered extensive abuse despite initial management skepticism, which was then extended to other regions like Asia, revealing similar fraudulent practices involving colluding employees and service agents.

As you can see from this warranty fraud taxonomy and these case studies, these aren’t edge cases. They’re mainstream manufacturers dealing with systemic fraud inside their own networks.

The Hidden Threat to Your Bottom Line: How Sales Fraud is Bleeding Your Business Dry

4. How Should Manufacturers Protect Their Revenue From Warranty Fraud?

The good news? You don’t have to accept warranty fraud as a cost of doing business. A comprehensive control framework works when it’s implemented with intent.

a. Contracts

Clear, standardised terms that define coverage and service entitlements. Include audit rights and anti-fraud clauses to keep dealers and providers honest.

B. Transaction Controls

Validate customer entitlement and claim legitimacy every time. Automate material returns control. Layer in analytical scoring so high-risk claims get flagged early.

C. Analytics

This is where the magic happens. Combine business rules, anomaly detection, predictive models, and even social network analysis to spot patterns of collusion. Fraudsters aren’t random — their footprints are there if you look.

D. Service Network Management

Benchmark your dealers, agents, and providers. Use performance dashboards, mystery shopping, and audits to keep them accountable. Service networks are fertile ground for fraud — manage them like the strategic assets (and risks) they are.

red stop sign highlighting that it is possible to prevent and detect revenue leakage through warranty fraud and abuse. — Photo by Pixabay on Pexels.com

Conclusion: Stop the Silent Margin Killer

Warranty fraud is more than an operational headache — it’s a direct attack on your revenue, your forecasts, and ultimately your valuation. If you wouldn’t tolerate a 5% revenue leak from your supply chain, why are you tolerating it from warranty fraud?

As executives in manufacturing and medtech, you have two choices:

Treat warranty fraud as an unavoidable cost and keep bleeding margins.
Or treat it as a strategic risk — implement controls, demand analytics, and take back control of your revenue.

Personally, I know which choice makes your next board meeting easier.

Ransomware Attacks on R&D Companies Explained

Posted on September 2, 2025 by Paul Curwell

Paul Curwell

5–8 minutes

3 Key Takeaways:

Ransomware has professionalised: today’s gangs follow an 8-step targeting cycle that looks more like a military operation than a cybercrime.
R&D-intensive companies are prime targets because weak data governance creates exploitable security gaps — and attackers know your research is the fastest route to a big payday.
The financial impact goes far beyond ransom payments — share prices fall, investors back away, and patents can be undermined.

The impact on your business

Ransomware is the digital version of kidnapping. Attackers break into your systems, lock up your data, and demand payment for its release. But unlike old-school kidnappers, they don’t just keep the hostage — they copy it too. For R&D-heavy companies, that hostage is your research pipeline: your trade secrets, trial data, and commercialisation plans.

And here’s the part too many boards miss: the ransom is only the start of the damage.

Share price impact: Public disclosures of ransomware routinely knock 3–5% off market cap. One company’s 2023 breach wiped millions in value overnight.
Investor attraction: If you can’t prove your research data is safe, investors won’t touch you. Due diligence now treats ransomware resilience like another line in your balance sheet.
Time-to-market delays: Every month of R&D delay costs millions in burn and kills first-mover advantage. In pharma, a six-month delay can add $3–6M to costs.
Commercialisation risk: Stolen formulas and trial data can create “prior art” that undermines your patents. Translation: your billion-dollar IP is now legally copyable.

Ransomware isn’t just an IT outage — it’s a strategic risk to valuation, market entry, and investor confidence.

49% of Private Equity deals fail because of undisclosed data breaches

Why R&D-intensive companies are vulnerable

Think of your R&D program as a fragile supply chain. Every stage — discovery, trials, data integrity, and commercialisation — depends on governance and control. When ransomware strikes, the weak links show.

Here’s an uncomfortable truth: in R&D intensive businesses, many ransomware vulnerabilities come not from exotic zero-day cyber exploits but from poor data governance, which flows through to your information security posture. Data governance is not a “tech” term — it’s a board-level responsibility. When governance fails, attackers thrive:

Unclear ownership and access: If no one owns the data, no one protects it. Attackers love overexposed research folders and outdated VPN access.
Failed backups: Governance blind spots mean backups aren’t tested — so the first time you discover they don’t work is during an attack.
Misapplied controls: Without proper data classification, security teams guard low-value data while leaving crown jewels exposed.
Regulatory exposure: Weak governance makes GDPR, HIPAA, or ISO non-compliance almost inevitable — and regulators don’t accept “we were hacked” as an excuse.
Slow detection: Without adequate security monitoring, attackers can sit inside your network for weeks undetected, rehearsing their attack.

Poor governance contributes to a perfect operating environment for ransomware groups. And in R&D-heavy sectors, that means your valuation is basically gift-wrapped for attackers.

governance is key to protecting your data, data integrity, and implementing fit for purpose security protocols to guard against ransomware.

The professionalisation of ransomware in 2025: the 8-step targeting cycle

Forget the old “spray and pray” model where attackers blasted out phishing emails and hoped someone clicked. That was cybercrime’s stone age, and focused on everyone and everything rather than being highly sophisticated, targeted, and selective.

Today’s ransomware gangs are professionals. They behave like organised crime syndicates, following a structured 8-step targeting cycle designed to maximise pressure and payouts:

Target Selection – Industries where data equals enterprise value, such as pharma, biotech, semiconductors, medtech, and advanced manufacturing.
Initial Surveillance – Public sources, leaked credentials, and open servers help attackers map your weak spots.
Final Target Selection – They zoom in on firms with high-value IP, fragile governance, and patchy defences.
Pre-attack Surveillance – Once inside, they quietly watch. Mapping networks, spotting backup systems, and studying user behaviours.
Planning – With insider-level intel, attackers script their playbook for maximum damage and leverage.
Rehearsal – Yes, they practice. In test environments, they run through encryption and data theft to ensure nothing goes wrong on game day.
Execution – Systems are locked, IP is exfiltrated, ransom notes drop. Victims are blindsided; attackers are already two steps ahead.
Escape & Evasion – Logs are wiped, trails covered, backdoors left behind for future profit.

Paul Curwell's 8-step targeting cycle for organised crime

This is not opportunistic crime conducted by pimply teenagers. It’s deliberate, researched, and ruthlessly commercial — closer to an IPO roadshow than a smash-and-grab.

Case studies: when ransomware hit the labs

Perhaps your one of the many people I talk to at industry events who’s sick of hearing about security. Well, if you need further convincing on the importance of this topic here are 5 real-world examples that show how professionalised ransomware plays out:

Company	Attacker Group	Success Factors	Business Impact	IP/Patent Risk
Company A (India, 2023)	ALPHV / BlackCat	Compromised VPNs & stolen credentials, extensive pre-attack surveillance.	17TB of data stolen, 3–5% share price drop, $50–62M revenue hit, $3M+ recovery costs.	Risk of patent invalidation if leaked as prior art.
Company B (Japan, 2023)	Unnamed (likely RaaS affiliate)	Supply chain intrusion, privileged access exploitation.	Multi-week disruption of R&D and manufacturing, investor concern.	Possible exposure of neuroscience research.
Company C (India, 2020)	Unnamed criminal ransomware group	Phishing & credential theft during COVID-19 trials.	4% share price drop, 2-week trial delays, $150k–$250k added burn per project.	Trial data exposure undermines exclusivity.
Company D (Germany, 2023)	Unnamed RaaS affiliates with APT links	Exploited enterprise / cloud vulnerabilities, targeted R&D repositories.	Attack contained quickly, limiting share price impact.	Potential R&D data exposure, though managed.
Company E (UK, 2024/25)	Qilin	VPN / firewall exploits (CVE-2024-21762), targeted NHS-critical systems.	£32.7M loss (~$41M), weeks of disruption, ransom ~$50M.	Diagnostic IP exposed, R&D collaborations disrupted.

The costs of an IP breach

Conclusion: the strategic picture

The uncomfortable truth: ransomware groups have professionalised faster than most boardrooms have adapted. They’re running playbooks that look like government intelligence operations, and they’re aiming squarely at industries where research is the business to make sure you’re highly incentivised to pay up.

If you’re in an R&D-intensive sector, you’re not just another target — you’re the main course. Weak governance, patchy security, and misplaced confidence in cyber insurance won’t save you.

So, next time someone in the boardroom calls ransomware an “IT problem,” remind them it’s actually a governance problem. Because in 2025, the attackers aren’t amateurs anymore — and if your business wants to survive your response can’t be either.

MedTech Companies Are Losing Millions to Revenue Leakage Without Knowing It

Posted on July 29, 2025 by Paul Curwell

Paul Curwell

6–8 minutes

3 Key Takeaways

MedTech companies lose 5-7% of gross revenue to fraud, supply chain leakage, and contract failures—most executives don’t even know it’s happening
Your supply chain integrity is under attack from unauthorised discounting, billing fraud, and channel partners who bend the rules
Revenue protection isn’t a back-office problem—it’s a strategic risk that directly impacts your bottom line and company valuation

You’re Bleeding Money and Don’t Even Know It

Here’s a sobering thought: while you’re obsessing over R&D budgets and production efficiency, your company is probably hemorrhaging 5-7% of gross revenue through fraud and supply chain leakage. That’s not a typo—it’s reality.

I discovered this harsh truth during recent work in the MedTech sector. Frankly, I was shocked. Through discussions with colleagues and clients about these estimates, I realised many executives either don’t recognise this problem or dramatically underestimate its impact.

Consider this: if your company generates $100 million annually, you’re potentially losing $5-7 million to preventable leakage.

That’s serious money—money that should be flowing straight to your bottom line instead of disappearing into the supply chain black hole.

The Billion-Dollar Problem Nobody Talks About

Revenue leakage in healthcare equipment and medical device manufacturing isn’t some theoretical concern. Industry data shows pharmaceutical companies collectively lose over $15 billion annually from rebate abuse and chargeback errors alone. Medical device companies face identical risks with even less protection.

The gross-to-net gap—the difference between what you bill and what you actually receive—reached $236 billion across healthcare in 2021. While pharma companies were forced by regulation to build revenue controls, medical device and diagnostic equipment manufacturers are still catching up, despite facing identical complexity.

Here’s why this matters to your bottom line: unlike other business costs, revenue leakage is almost entirely preventable. Every dollar you recover from leakage flows directly to profit. No additional manufacturing costs, no new R&D investment—pure margin improvement.

Where Your Money Disappears: The Top Leakage Points

Revenue vanishes at multiple stages throughout your operation. Understanding these vulnerabilities helps you plug the holes:

Manufacturing & Procurement Losses

Quality failures: Rejects and recalls from substandard components can trigger millions in losses
Supply chain fraud: Counterfeit parts compromise your supply chain integrity while creating warranty claims
Contract mismanagement: Poor supplier agreements allow pricing discrepancies to compound over time

Just last week, I heard a podcast about MedTech product packaging for air transport. The extreme temperature swings in aircraft cargo holds—from scorching tarmacs to sub-zero altitudes—can destroy highly calibrated diagnostic equipment. These “invisible” logistics failures create expensive writeoffs that directly impact revenue.

Channel stuffing fraud – a distribution problem

Distribution & Channel Partner Issues

Unauthorised discounting: Partners who exceed agreed discount limits without approval
Product diversion: Legitimate products sold outside authorised territories or channels
Contract violations: Distributors who bend pricing rules or ignore territorial restrictions
Billing errors: Complex pricing structures create opportunities for mistakes that favor customers

Product Diversion in the Healthcare Supply Chain: What’s the Problem and How Big Is It?

Sales & Service Revenue Gaps

The complexity of healthcare equipment pricing creates multiple leakage points:

Revenue Stream	Common Leakage Points
Equipment Sales	Unauthorised discounts, pricing errors
Service Contracts	Underpriced renewals, forgotten billing
Software Licenses	Unauthorised usage, poor compliance tracking
Diagnostic Consumables	Volume discrepancies, rebate abuse
Training Services	Unbilled hours, contract scope creep

The Hidden Threat to Your Bottom Line: How Sales Fraud is Bleeding Your Business Dry

MedTech is More Vulnerable Than Pharmaceuticals

Through my recent work, I’ve seen how medical device and diagnostic equipment companies face unique structural challenges that make revenue leakage worse:

Business Model Complexity: While pharma sells discrete products through standardised channels, MedTechs manage intricate bundles. A single “sale” might include equipment leasing, maintenance contracts, software licenses, training services, and ongoing consumables—each with different pricing structures and discount schedules.

Fragmented Distribution: MedTechs rely on more diverse partner networks than pharma companies. Specialised dealers, regional distributors, service providers, and system integrators all have custom contract terms and varying compliance capabilities.

Legacy Revenue Controls: The MedTech and diagnostic equipment sector has been slower to implement systematic revenue controls. While pharma companies invested heavily in rebate management and contract compliance systems under regulatory pressure, many healthcare equipment manufacturers still operate with outdated processes.

This complexity creates opportunities for revenue to slip through cracks that pharma companies sealed years ago.

Building Your Revenue Defense System

Protecting revenue requires systematic action across multiple areas. Here’s what works:

1. Implement Real-Time Monitoring

Install automated systems that flag unusual discount patterns
Set up alerts for pricing exceptions that exceed thresholds
Monitor partner sales data for territorial violations or volume discrepancies
Track service contract renewals to prevent revenue gaps

2. Strengthen Contract Controls

Automate discount approvals with clear escalation paths
Build dynamic pricing systems that adjust for market changes
Create partner scorecards that track compliance metrics
Implement regular contract audits beyond just financial reviews

3. Enhance Supply Chain Integrity

Deploy serialisation and track-and-trace technologies
Validate partner credentials and monitor their performance
Create digital twins that link physical inventory to service claims
Establish rapid response protocols for integrity breaches

4. Data-Driven Partnership Management

Cross-reference sales transactions, service logs, and rebate submissions
Use analytics to identify patterns that indicate fraud or process failures
Reward partners for validated outcomes, not just volume metrics
Conduct operational audits that assess pricing integrity and territorial compliance

Product Serialisation – a tool to help counter diversion and illicit trade

The Board-Level Questions You Need to Ask

Revenue protection belongs on your executive agenda. Start asking these questions:

What’s our independently verified leakage rate?
Can we trace our products through their entire lifecycle?
Do we have complete visibility over channel partner behavior?
Who specifically owns revenue protection accountability?
Are we prepared for regulatory scrutiny on supply chain integrity?

If you can’t answer these questions clearly, that’s where your risk lives.

Your Next Steps: Stop the Bleeding

Revenue leakage is fixable. Companies that address it proactively enjoy stronger margins, reduced risk exposure, and better competitive positioning.

Start with these immediate actions:

Week 1: Audit your last quarter’s discount exceptions and pricing variances. Calculate the financial impact of irregular patterns.

Month 1: Implement automated alerts for pricing exceptions that exceed your predetermined thresholds. Review partner compliance with territorial and discount agreements.

Quarter 1: Deploy analytics tools that cross-reference sales data, service logs, and rebate submissions to identify anomalies.

Year 1: Build comprehensive revenue protection systems with real-time monitoring, automated controls, and regular partner audits.

The companies moving first will capture disproportionate advantages while competitors struggle with eroded margins. In an industry where innovation drives growth but operational excellence determines profitability, revenue protection has become a competitive necessity.

Your money is disappearing right now. The question is: what are you going to do about it?

Ready to plug the revenue leaks in your organisation? Start by conducting a comprehensive revenue audit to identify your biggest vulnerability areas. The sooner you act, the sooner you’ll see those lost millions flowing back to your bottom line.

AI for Deeptech Startups: Balancing Speed and Security

Posted on June 24, 2025 by Paul Curwell

Paul Curwell

7–10 minutes

Key Takeaways

AI is already deeply embedded in how R&D startups operate—handling analysis, reporting, quality monitoring, and workflows.
But every tool and integration you use—especially if ungoverned—can expose your intellectual property (IP) or sensitive data.
Protection doesn’t mean overengineering—startups can use lean frameworks and smart defaults to stay secure without losing momentum.

You’re already using AI—but are you protecting what matters?

If you’re leading a biotech, medtech, advanced manufacturing, or deeptech startup, AI is probably already hard at work in your business. Whether you’re using your LIMS to track experimental data, automating lab tasks with tools like Zapier or N8N, or generating regulatory reports with ChatGPT, you’re benefiting from AI’s ability to deliver speed, insight, and productivity.

And it’s working. You’re innovating faster, making better decisions, and doing more with fewer resources. That’s exactly what investors and partners want to see from early-stage companies. In 2025, you don’t need a 500-person team—you need smart systems.

But the same technologies accelerating your work can also quietly undermine it. If you’re not actively managing how AI interacts with your intellectual property (IP) and sensitive data, you’re leaving the door wide open for mistakes, leaks, or compliance failures that can stall your growth—or sink your business entirely.

Protecting Your R&D When Outsourcing Rapid Prototyping

How AI Is supercharging R&D-intensive startups in 4 use cases

AI isn’t just hype for small innovators—it’s a practical tool delivering real business outcomes. And unlike larger enterprises that spend millions and deploy large teams integrating AI into legacy systems, deeptech SMBs are cloud-native and agile. That gives you a major edge.

Here’s how I see most small, research-driven teams using AI right now:

1. Data Collection and Analysis

Your scientific and engineering teams are automating the aggregation of experimental results, integrating data from sensors, lab systems, and external research. AI helps clean, normalize, and interpret it all—so decisions can be made in days, not months.

You’re also leveraging AI for literature mining and competitive analysis, giving your team a clearer picture of where to focus and how to differentiate.

2. Continuous Control and Quality Monitoring

Whether you’re a medtech firm tracking calibration drift or a materials science startup checking for outliers, AI is helping detect inconsistencies early. This kind of real-time feedback loop improves reproducibility and protects your reputation with regulators and partners.

3. Reporting and Documentation

Grant milestones, regulatory submissions, investor updates—these all take time. AI-generated summaries, charts, and reports help your team stay compliant and communicative without pulling attention away from the actual science.

4. Workflow and Service Management

Your operations are already automated. Zapier, N8N, and Power Automate are running the back office: scheduling lab time, flagging inventory shortages, tracking project milestones. AI helps orchestrate and optimize these workflows so your team stays productive.

This all adds up to serious efficiency gains. But—and it’s a big but—each of these systems and integrations touches sensitive data or protected IP. And that’s where the real risk creeps in.

Protecting Innovation: The Spectre of Trade Secrets Theft in Biotech

Four AI risks most science and tech startups overlook

These are excellent use cases, but like everything, there are pros and cons. Deeptech’s need to understand how AI tools and use cases can generate downside risk for your business:

1. Trade Secrets Floating in the Open

AI models are great at summarising documents and drafting content. But paste your prototype results or lab logs into an unsecured LLM, and you might be training someone else’s model with your trade secrets.

This isn’t a fringe issue. In 2023, employees of one global tech company accidentally leaked sensitive source code through ChatGPT. They were trying to be efficient—but exposed high-value IP instead.

Case Study 1: Global tech’s ChatGPT Blunder: IP Exposure Through Misunderstanding

In 2023, engineers pasted sensitive source code and internal meeting notes into ChatGPT while trying to solve coding problems. They didn’t realise that public AI tools could store and retain this input.

The result? Confidential trade secrets exposed. The company responded by banning the use of generative AI internally. But the damage was done.

Lesson: If your staff don’t understand how AI tools process and retain information, they may accidentally train someone else’s model with your crown jewels.

Practical actions:

Identify what qualifies as a trade secret in your business. Write it down.
Turn off chat histories in AI tools or use private models.
Avoid pasting raw R&D data or code into consumer AI platforms.

2. Data Leaks Through Automation Tools

Automation platforms like Zapier, Make, and N8N are amazing for productivity—but they’re often invisible to risk and compliance teams. If data is moving between systems without encryption or logging, that’s a blind spot.

One startup had lab results automatically emailed to a shared inbox via Zapier. Harmless? Until one of those emails ends up forwarded to the wrong contact triggering a data breach incident.

Case Study 2: Global tech company’s AI Team Accidentally Exposes 38TB of Data

In another 2023 case, another big tech’s own AI research team uploaded a GitHub repo with an incorrectly configured Azure SAS token. This gave public access to 38TB of internal data—including private research, credentials, and backups.

This wasn’t a cyberattack. It was a configuration error—just one line of code—and it put an entire research group’s IP at risk.

Lesson: Even world-class AI teams can slip up if access controls and cloud permissions aren’t managed carefully.

Practical actions:

Audit your integrations quarterly. Know where data is flowing.
Limit the exposure of sensitive data in workflows.
Apply the same scrutiny to no-code tools as you do cloud providers.

The Rising Threat of Cyber-Enabled Economic Espionage: What Business Leaders Need to Know

3. Misconfigured Cloud Environments

Being cloud-native doesn’t mean being secure. Startups often move quickly, spinning up instances, sharing buckets, and adding users without much structure. The result? Sensitive IP and research data sitting in misconfigured storage with public access enabled.

Case Study 3: Biotech’s AI Feature Abused to Extract Genetic Data

Attackers didn’t hack the biotech’s core systems. They reused leaked credentials to log into user accounts and exploited the company’s DNA Relatives feature—powered by AI—to harvest massive amounts of genealogical and genetic data.

The breach wasn’t about a flaw in the AI—it was about poor monitoring and a lack of foresight into how AI-powered features could be abused at scale.

Lesson: AI features can scale risk just as fast as they scale value. You need visibility and governance to keep both in check.

Practical actions:

Use native controls like IAM, DLP, and logging in AWS, GCP, or Azure.
Review access privileges regularly—especially after staff or contractor changes.
Don’t assume your default setup is safe—check it.

4. Regulatory Risk and Data Sovereignty

If you’re collecting personal or regulated data—think clinical trial results, biospecimens, or identifiable research participant data—you’re accountable under privacy laws. And regulators won’t accept “we’re a startup” as an excuse.

Practical actions:

Store regulated data in compliance with local data laws.
Map where your data lives and who can access it.
Delete data you no longer need—less data, less risk.

You Don’t Need an Army—You Just Need a Plan

Information security and data protection doesn’t have to be expensive or complicated. You just need to know what matters most—and build guardrails that suit your size and stage.

That’s why frameworks like SMB1001 exist. Designed for small, R&D-heavy businesses, it gives you a clear path to understanding what’s critical, setting sensible access controls, and documenting how you manage risk—all in a way that supports growth, not bureaucracy.

You don’t need ISO 27001 on day one. But you do need to show investors and partners that your IP and data aren’t flying blind through a tangle of automations and unvetted tools.

The 3 SMB Risk Management frameworks you need to protect your business

Final Thoughts: AI Is Fuel for Growth—If You Protect the Engine

AI is your multiplier. It helps small teams outperform larger competitors, serve customers faster, and bring complex products to market on a startup budget.

But if your trade secrets leak or research data ends up in the wrong hands, that advantage disappears overnight. Worse, you might not even know it’s happened until it costs you a deal, a grant, or a key staff member.

So if you’re using AI—and I know you are—take these three steps now:

Map where your IP and sensitive data live.
Review how they flow through AI and automation tools.
Use a framework like SMB1001 to set practical controls that grow with you.

The best part? Once you’ve got this in place, you’re not just secure—you’re investable, credible, and ready to scale.

Unlocking New Uses for your SIEM: Beyond Cybersecurity

Posted on May 6, 2025 by Paul Curwell

Paul Curwell

7–11 minutes

3 key takeaways:

Most companies are sitting on powerful analytics platforms like SIEMs—but rarely use them beyond cyber.
There’s untapped potential to apply these tools to fraud, insider threat, IP protection, and compliance monitoring.
With the right strategy, businesses can reduce compliance costs, improve visibility, and make better investment decisions.

Why this matters

Today’s risk environment demands more from businesses than ever before. Whether you’re protecting sensitive R&D, complying with complex regulations, or trying to prevent fraud, the traditional playbook is falling short. Organisations invest millions in security analytics. Frequently though, use of these tools happens in a silo, begging the question “can’t they do more?”. That’s a missed opportunity.

Many organisations already own high-powered Security Information and Event Management (SIEM) and observability platforms to give rich, real-time operational insights. In most businesses, there is no use of these tools outside of cybersecurity. That’s where this story begins.

The landscape: SIEMs, observability tools, and everything in between

The tooling landscape in most medium-to-large enterprises is complex, but it’s also full of hidden potential. From security teams running SIEMs to engineers monitoring application performance, there are multiple systems collecting, storing, and analysing valuable data. The challenge is most businesses don’t fully understand how these platforms differ—or how they complement one another.

Let’s unpack the main types of platforms:

Security Information and Event Management (SIEM) – These platforms are the backbone of many security operations centres. SIEMs like Splunk, Sentinel, and Elastic collect and correlate security events to find and respond to threats in real time. They’re also critical for compliance reporting, audit trails, and forensic investigations.
Observability platforms – Tools like Datadog, New Relic, and OpenTelemetry provide deep insights into how systems are operating. Used by DevOps and Site Reliability Engineers, they collect metrics and logs to monitor system health, performance, and prevent outages.
Data lakes and warehouses – These centralised platforms are great for long-term storage and complex data queries. However, they often lack the speed or alerting capability needed for real-time risk response.
BI dashboards and analytics tools – Platforms like Power BI and Tableau provide strong visualisation for decision-making. They focus on historical data, not real-time detection.
Log management platforms – Tools like ELK store data for troubleshooting, but don’t get integrated into business processes.
Application Performance Monitoring (APM) tools – Focus on user experience and technical metrics but often miss the business context needed for enterprise insights.
Custom threat intelligence platforms – Powerful in capable hands, but often resource-intensive to maintain and inaccessible to non-technical teams.

Understanding how these tools work—and where they overlap—opens up new opportunities for extending their use into fraud, compliance, and continuous monitoring.

Non-cyber use cases hiding in plain sight

What became clear through my research is that many businesses are unknowingly sitting on a goldmine of data. This data can improve resilience, situational awareness and decision quality, resulting in reduced losses. Many tools already have access to the underlying telemetry. The gap is that organisations don’t translate their use cases into language or workflows these systems can use to solve business or compliance problems.

Here are a few real-world examples of how some organisations are using their existing telemetry platforms to solve non-security problems:

Fraud detection – One financial services firm used their SIEM to detect behavioural anomalies in user logins and transaction data. This helped identify fraudulent activity faster and reduce false positives in fraud alerts.
IP protection – A biotech set up observability pipeline alerts to detect unusual access patterns to protected research environments. This gave them a chance to intervene before valuable data walked out the door.
Insider threat monitoring – A large enterprise integrated HR systems with SIEM logs to flag when high-risk employees (e.g. those about to exit the company) accessed sensitive files, enabling pre-emptive action.
Physical security integration – A logistics company ingested building access logs into their SIEM to monitor for suspicious after-hours activity. This provided near real-time visibilty of threats in zones containing high-value or regulated assets.
Regulatory compliance – A US health services provider configured automated alerts to detect improper access to patient records. This streamlining HIPAA compliance and reporting, easing the burden on their audit teams.

These examples aren’t outliers. They represent what’s possible when organisations look beyond the traditional cyber perimeter and align technology with broader business risks.

Alert management and insider risk continuous monitoring systems

Trade-offs and tricky bits

Of course, extending the use of SIEMs and observability platforms isn’t without its challenges. These are powerful tools, but were built with specific users and functions in mind. Repurposing them for broader use requires careful planning, stakeholder alignment, and a realistic view of limitations.

Metric	Considerations
Cost vs return	SIEM platforms, in particular, can become prohibitively expensive as more data sources are added. Every additional log source or telemetry stream can drive up ingestion costs, licensing fees, and infrastructure requirements. Businesses need to balance the value of added insights against escalating costs.
Expertise and resourcing	Many of these platforms are complex and require specialist skills to configure and manage. Cyber teams are often already overstretched, they don’t have capacity. Asking them to support fraud, compliance, or operational use cases often requires cross-skilling or additional resources.
Data governance and privacy	Aggregating sensitive business data—such as HR records, payroll, or personnel movements—can raise privacy concerns. Any use needs to be aligned with data protection laws such as Australia’s Privacy Act, or the GDPR in Europe.
Tool mismatch and workflow gaps	Observability platforms are fast, lightweight, and built for performance. But they’re not designed for legal defensibility, long-term retention, or audit-ready compliance reporting. SIEMs, on the other hand, are great for that. But, they can lack the ease of use or responsiveness that observability tools provide.
Redundancy and duplication	Without coordination, multiple teams end up collecting and analysing the same data using different tools. This can lead to inefficiency and potential confusion around ownership and accountability. Worst case for regulatory compliance, you generate contradictory records which is a red flag to an inspector.

Table: Benefits and Challenges

Yes, there are challenges, but the opportunities are too great to ignore. Now’s the time for risk and compliance leaders seeking smarter, scalable approaches to assurance to speak to the CIO.

Crafting Security Business Cases for Executive Buy-in

Real compliance benefits—if you play it right

Compliance is a growing cost centre for many organisations. Increasingly, fraud and protective security is becoming a regulated compliance program. Take Australia’s Privacy Act, Scams Protection Framework Act and Security of Critical Infrastructure Act as two examples. Teams are under pressure to meet complex compliance obligations, conduct audits, investigate incidents, and coordinate a response. Given most responses increasingly relate to compliance obligations, there’s a regulatory imperative to get this right. They’re often using manual processes and disconnected systems to do this, taking time, effort and higher chance of errors.

This is where SIEM and observability platforms can play a much bigger role. By automating key controls organisations can reduce the manual workload on compliance and audit teams. Examples include detecting access to sensitive data, validating privileged user activity, or monitoring export-controlled environments. The result? Improved productivity, cost control, and compliance. Dashboards and real-time alerts eliminate the need for manual reviews, reduce investigation time, and improve coordination across the business.

These platforms also provide strong evidence for legal and regulatory inquiries. For example, access logs and alert histories makes it easier to prove data segregation or show controls were in place. This supports compliance SOX, the Privacy Act, or Australia’s Security of Critical Infrastructure Act (SOCI).

These tools allow compliance teams to shift from reactive policing to proactive risk reduction. In turn, this makes them more efficient, more strategic, and more valuable to the business.

Developing an compliance obligation register for your business

What business leaders need to do next

This isn’t just a technology issue—it’s a business opportunity. Executives should be asking how they can leverage their existing technology investments to solve new problems.

Here’s a five-step path to get started:

Audit your existing tools – Inventory the telemetry and analytics platforms already in use. Identify whether you have a SIEM, an observability platform, or both. Are you using these to good effect?
Map broader risks – Work with fraud, HR, IP, and compliance stakeholders to identify high-impact, high-cost business risks. Identify use cases that benefit from automation and real-time monitoring.
Engage privacy and legal early – Involving these teams from the outset. This helps prevent delays later and ensures any solution aligns with data protection laws and internal governance frameworks.
Pilot a use case – Choose one low-risk, high-impact use case (e.g. unusual access to critical systems) and configure alerts or dashboards using existing tools. Track the cost, value, and effort involved.
Build the business case – Quantify what value these solution will save in hours, cost or loss reduction, or productivity. Present this in a way that links directly to business strategy and financial performance.

If you’re already paying for the Ferrari, why are you only using it for trips to the supermarket? With a little tuning and creativity, you can unlock value across new use cases without buying yet another tool.

Battling Industrial Espionage: TSCM Strategies for Deeptech

Posted on April 22, 2025 by Paul Curwell

Paul Curwell

5–7 minutes

Key Takeaways:

The deeptech competitive landscape is supercharging industrial espionage, with AI, semiconductors, and fusion energy as primary targets
Technical Surveillance Countermeasures (TSCM) now requires a dual approach covering both traditional bugs and compromised personal devices
Companies lose an estimated $500 billion annually to trade secret theft, with a 1,300% increase in semiconductor-related espionage cases since 2014

The Silent War for Innovation

I’ve spent the last decade advising companies on protecting their intellectual property, and I can tell you with absolute certainty: we’re in the middle of an unprecedented espionage arms race. The battleground? Your company’s deeptech innovations. The weapons? Everything from traditional listening devices to the smartwatch on your chief researcher’s wrist.

Anthropic’s recent update to their Responsible Scaling Policy highlights this shift, incorporating advanced Technical Surveillance Countermeasures (TSCM) to protect their AI trade secrets from increasingly sophisticated threats. They’re not being paranoid—they’re being prudent. Let me show you why your company should follow suit.

The Competitive Deeptech Landscape

The stakes in today’s innovation race extend far beyond simple market share. We’re seeing three critical battlegrounds emerge:

First, hardware sovereignty has become a national security concern.

Semiconductor independence drives geopolitical power, with AI infrastructure demands accelerating the race.
This isn’t just business—it’s realpolitik playing out in corporate boardrooms.

Second, energy constraints present both challenges and opportunities.

Nuclear fusion research has become intertwined with AI advancement, with ML algorithms accelerating materials science breakthroughs.
Energy constraints—such as limited electricity supply, high energy costs, or insufficient grid infrastructure—have a significant impact on the pace and scope of AI advancement for both countries and businesses.

Third, data frontiers represent the new oil.

Real-world biological, medical, and sensor data are becoming essential for training next-generation AI models.
Companies with unique datasets enjoy a 2-3x valuation premium compared to competitors with similar technology but inferior data.

These converging forces create perfect conditions for industrial espionage on an unprecedented scale.

Recent Security Incidents: When Theory Becomes Reality

The semiconductor industry provides the clearest examples of modern corporate espionage:

Case 1: From 2016-2020, a nation state-backed company orchestrated what I consider the perfect modern heist againt a competitor (the Original Equipment Manufacturer, OEM). The nation-state backed company recruited three OEM engineers with 200-300% salary increases, who walked out with IP valued between $400 million and $8.75 billion. The files were hidden on air-gapped laptops, making them undetectable until authorities raided the competitor’s offices.

Case 2: Even more concerning is what happened at another company, where nation-state backed hackers maintained access for 2.5 years, steadily exfiltrating chip designs used in Apple Pay and automotive systems. The victim firm only discovered the breach after significant damage was already done.

Case 3: Perhaps most alarming is company 3’s experience—this firm faces thousands of security incidents annually, including successful thefts of extreme ultraviolet lithography blueprints, technology that costs billions to develop and represents the cutting edge of semiconductor manufacturing.

These aren’t isolated incidents—they represent a systematic campaign to shortcut R&D timelines and undermine technological leadership.

How is confidential information compromised?

TSCM’s Dual Role in Modern Security

Technical Surveillance Countermeasures (TSCM) have traditionally focused on detecting physical bugs and wireless transmitters in sensitive spaces. This remains essential—but wildly insufficient in today’s threat landscape.

Modern TSCM must address two distinct but interconnected domains:

Traditional Counter-Eavesdropping:

Regular facility sweeps using spectrum analyzers and non-linear junction detectors
Physical security red-teaming to test facility vulnerabilities
Event-specific sweeps after high-risk meetings or suspected breaches

Cyber-Physical Convergence Threats:

Employee devices infected with malware that turns smartphones into always-on microphones
Wearables with speech-to-text capabilities silently uploading sensitive conversations
Supply chain implants that create hardware backdoors in seemingly innocent peripherals

Threat Vector	Example	Impact
Compromised devices	Malware turning smartphones into always-on mics	Real-time conversation monitoring
Wearables	Speech-to-text enabled smartwatches/glasses	Silent data exfiltration
Supply chain implants	Tampered peripherals with hardware backdoors	Persistent network access

Anthropic’s approach includes all of the above, plus deception technologies like honeypot model weights to identify and trace information leakage.

An Effective Information Security Strategy for 2025

Your company’s approach to protecting trade secrets must evolve beyond traditional cybersecurity and physical security silos. Here’s what works:

Integrated Defence Systems: Combine physical TSCM sweeps with network traffic analysis and endpoint monitoring. The segregated security approaches of the past create dangerous blind spots.
Zero-Trust Device Policies: Use of clearly designated zones where personal devices are prohibited is increasingly normal. This isn’t surveillance—it’s survival. The Princeton Plasma Physics Laboratory now requires all personal electronics be secured in Faraday pouches before entering research zones.
Supply Chain Verification: As the company in Case 3 discovered, vendor security (supply chain security) is your security. Implement hardware authentication mandates and binary authorisation frameworks for all incoming equipment and software.
Insider Threat Programs: Case 1 illustrates how easily employees can become vectors for IP theft. Modern insider threat programs should focus on behavioral analytics rather than punitive measures, identifying unusual data access patterns before information walks out the door.
Deception Technology: Following Anthropic’s example, plant convincing but subtly incorrect information in non-critical systems. When this data appears elsewhere, you’ve identified a leak.

A robust security program does not funciton in silos – it needs to present a holistic, complete treatment of the risk and address the particular threats faced by the respective organisation.

The Rising Threat of Cyber-Enabled Economic Espionage: What Business Leaders Need to Know

Conclusion: Security as Competitive Advantage

The commercialisation of deeptech innovations increasingly depends not just on who develops the technology first, but who can keep it secure long enough to bring it to market. While the FBI reports a 1,300% increase in industrial espionage cases since 2014, the companies succeeding in this environment aren’t necessarily the ones with the best technology—they’re the ones that can actually keep that technology secret.

An integrated approach to trade secret protection isn’t just good security practice—it’s a strategic business advantage. In an era where a single breakthrough in AI, semiconductors, or fusion energy could be worth billions, effective TSCM isn’t a cost center—it’s an investment in your company’s future.

The days of treating physical security, cybersecurity, and insider threats as separate domains are over. If you’re not addressing all three simultaneously, you might as well be posting your research on Twitter.

Your competitors have already figured this out. Have you?

The Hidden Threat to Your Bottom Line: How Sales Fraud is Bleeding Your Business Dry

Posted on April 8, 2025 by Paul Curwell

Paul Curwell

5–8 minutes

Key Takeaways:

Fraud costs companies 5% of annual revenue, with economic downturns increasing fraud risks
Sales teams present unique fraud vulnerabilities through returns schemes, revenue manipulation, and commission fraud
Implementing targeted controls like commission clawbacks and automated monitoring can protect your revenue and reputation

Introduction

Let’s face it – while you’re busy watching your supply chain for external threats, your sales team might be quietly bleeding your company dry. As someone who’s spent years investigating corporate fraud cases, I’ve seen firsthand how sales fraud schemes can fly under the radar while causing massive financial damage. In today’s shaky economic climate (thanks, tariffs), fraud is on the rise, and your sales department is particularly vulnerable.

The Costly Reality of Sales Fraud

Did you know that companies lose a whopping 5% of annual revenue to fraud? That’s billions collectively wasted across industries. What’s more alarming is that according to fraud experts, 55% observed increased fraud during economic downturns – precisely the environment we’re navigating now.

For business leaders and finance chiefs, this isn’t just a financial headache—it’s a direct attack on your business strategy and profitability. While you’re focused on protecting your trade secrets and IP protection from outside threats, your greatest insider threat might be sitting in your sales department.

Six Common Sales Fraud Schemes Killing Your Profits

1. Returns Fraud with Kickbacks

This scheme is particularly sneaky. A salesperson encourages customers to purchase excess inventory (often with unauthorized discounts) to inflate sales figures. Later, the customer returns the excess inventory, but the salesperson keeps their commission. Meanwhile, your inventory numbers and forecasting are completely thrown off.

Red Flags to Watch For:

Large sales orders followed by significant returns
Sales spikes near reporting periods (quarter-end) that reverse shortly after
High return rates for specific salespeople compared to others
Unusual relationships between sales staff and certain customers

In 2022, a global electronics distributor discovered a senior salesperson colluding with a key customer on bulk orders at steep discounts. After commissions were paid, the customer returned over 60% of the inventory. Pretty clever scam, right?

2. Revenue Recognition Fraud

This scheme involves manipulating revenue figures or pocketing unrecorded revenue. For example, an employee might issue a credit note and split the refund with a customer. For technology companies especially, recording revenue too early can artificially inflate performance metrics.

Red Flags to Watch For:

Customer receipts missing for completed sales
Same person handling both invoicing and payment collection
Unusual timing of revenue recording (especially at quarter-end)
Differences between contract terms and recorded revenue

Channel stuffing fraud – a distribution problem

3. Credit Note Manipulation

Your sales team might be issuing unauthorized credit notes to steal funds or hide theft. Without proper oversight, this fraud can continue for months or even years before anyone notices.

Red Flags to Watch For:

Credit notes issued without proper approval
Unusual patterns or increased frequency in credit note issuance
Credit notes that lack supporting documentation
Certain employees processing a disproportionate number of credit notes

4. Inventory Fraud

This classic scheme involves stealing stock via false sales or diverting goods in transit. In 2022, an employee at an Australian parts supply company altered supplier bank details to divert payments while covering up inventory theft through falsified invoices. Their research showed this could be prevented with better automated fraud detection tools.

Red Flags to Watch For:

Negative inventory entries or unexplained stock differences
Frequent cancellations of sales transactions
Differences between physical inventory counts and system records
Unusual shipping or delivery patterns

inventory in a warehouse — Photo by Tiger Lily on Pexels.com

5. Discount and Pricing Manipulation

In Asia, employees were caught receiving kickbacks for granting unauthorized discounts. This not only hurts your profits but can disrupt your entire pricing strategy and market positioning.

Red Flags to Watch For:

Discounts disproportionately benefiting specific customers
Patterns of excessive discounts tied to one salesperson
Discounts offered without proper approval or documentation
Unusual changes in profit margins across similar sales

6. Commission Fraud

Consider this simple example: if a salesperson fraudulently changes their commission rate from 10% to 20% on $1,000,000 in sales, that’s $100,000 straight out of your pocket. Multiply that across your sales team and years of operation, and you’re looking at potentially huge losses.

Red Flags to Watch For:

Cash skimming from sales that go unrecorded
Creating fake sales to inflate commission numbers
Differences between sales data and bank deposits
One salesperson consistently outperforming peers by unusual margins

Software vs. Physical Products: Different Risks

The selling of software brings its own unique fraud risks. While physical product fraud often involves inventory theft and returns, software sales fraud typically involves revenue manipulation and more complex schemes.

For software subscription companies, a common scheme involves selling discounted multi-year subscriptions to partners who later cancel most licenses after commissions are paid. One company discovered a regional manager had colluded with a reseller to inflate sales figures and split the commission.

For physical products, fraud detection may be easier due to inventory checks you can see and touch. Software fraud, however, can be harder to detect since the product isn’t physical. For instance, in 2021, a software company found that a sales manager sold discounted multi-year subscriptions to a partner who later canceled over 70% of the licenses within six months. The manager received commissions based on gross sales but wasn’t penalized for cancellations.

Returns Fraud – a risk for eCommerce companies

Protect Your Bottom Line: Four Action Steps

Implement Commission Clawbacks: Tie commissions to net sales (gross sales minus returns) and implement penalties for canceled subscriptions or returned goods. This single control can eliminate much of the motivation for fraud.
Create Stricter Approval Processes: Require manager approval for large discounts, bulk orders, or unusual contract terms. This creates accountability and transparency. For credit notes, implement a two-person approval system that prevents a single employee from handling the entire process.
Leverage Data Analysis: Monitor return rates by salesperson, product line, and customer using tracking tools. Look for patterns of excessive discounts followed by high return rates. Modern analysis can flag unusual activities long before traditional audits would catch them.
Conduct Regular Internal Audits: Focus on high-risk areas such as discounts, bulk orders, refunds, and return transactions. Surprise audits are particularly effective at catching ongoing fraud schemes.

Call to Action

Stop leaving your revenue vulnerable to insider threats. Review your sales controls today and implement these four steps to protect your bottom line. The economic landscape is already challenging enough without letting sales fraud drain your profitability. In my experience, most companies discover fraud only after significant damage has been done. Don’t wait for your technology investments and research efforts to be undermined by preventable financial losses. As business leaders, we can’t afford to overlook this hidden danger in our sales departments. Take action now before your next earnings report reveals the damage.

Protecting Your R&D When Outsourcing Rapid Prototyping

Posted on April 1, 2025 by Paul Curwell

Paul Curwell

5–7 minutes

3 Key Takeaways:

Outsourcing rapid prototyping is essential for speed and cost efficiency but poses serious trade secret and IP risks.
Real-world cases show that failing to protect your R&D can lead to trade secret theft, fraud, and competitive loss.
A proactive strategy—including legal safeguards, secure operations, and ongoing monitoring—can mitigate risks.

Rapid Protyping offers many benefits, but be sure to manage your risk

Outsourcing rapid prototyping is a game-changer for R&D-driven businesses. It accelerates innovation, slashes development costs, and opens doors to specialist skills and cutting-edge tech that would be costly to build in-house. With the global rapid prototyping market projected to soar from $3.33 billion in 2024 to over $21 billion by 2034, it’s clear that more businesses are embracing this approach to stay ahead of the curve. Fixing design flaws early during prototyping can be up to 100 times cheaper than post-release corrections—a compelling reason why prototyping is no longer a luxury, but a business imperative.

Types of Rapid Prototyping Techniques

Common prototyping methods include:

Stereolithography (SLA): High-detail resin printing.
Fused Deposition Modeling (FDM): Budget-friendly plastic extrusion.
Selective Laser Sintering (SLS): Durable powder-based prints.
Direct Metal Laser Sintering (DMLS): Precision metal parts.
CNC Machining: Subtractive manufacturing for high-strength components.

Each technique has its own supply chain risks, making security considerations essential from the outset.

But here’s the catch—outsourcing means sharing your most valuable assets: trade secrets, proprietary designs, and sensitive R&D data. Whether you’re working with a niche 3D printing firm or a global manufacturing partner, the risk of IP theft, insider threats, or accidental disclosure is real. In fast-moving industries like automotive, biotech, and consumer tech—where time-to-market is everything—balancing speed with security is critical. This article explores how founders can unlock the full potential of prototyping and outsourcing, while putting practical guardrails in place to protect their intellectual property and business viability.

The Need for Outsourcing Rapid Prototyping

Startups and SMEs often lack the in-house capabilities for advanced prototyping. Outsourcing helps by:

Cutting costs—no need for expensive machinery or full-time specialists.
Providing access to world-class expertise in emerging technologies.
Accelerating product development and market entry.

But with these benefits come significant risks. Handing over your prototype means exposing critical trade secrets to external partners—some of whom may not be as trustworthy as they claim.

Example of additive manufacturing used in rapid prototyping — Photo by FOX ^.ᆽ.^= ∫ on Pexels.com

Case Study: IP Theft in Outsourcing

A U.S. medical device startup learned this lesson the hard way. They outsourced prototyping to a foreign manufacturer, only to discover a near-identical product in the market months later. The culprit? Their own supplier, who exploited weak contractual protections to replicate and commercialise the design. The result: financial loss, legal battles, and an irreparably damaged competitive advantage.

Lesson learned? If you don’t protect your trade secrets, someone else will profit from them.

Startup Sabotage: A Trade Secret Theft Case Study & How to Protect Your Company

Understanding IP Protection for Prototypes

Trade Secrets vs. Patents

Patents are great—until they aren’t. They require public disclosure and take years to secure. Trade secrets, on the other hand, remain confidential as long as they are actively protected. Most prototypes fall under trade secrets because early-stage innovation needs secrecy, not immediate disclosure.

Copyright Protection

Copyright automatically applies to design files and software components. However, international enforcement can be tricky, making additional legal steps essential when working with overseas partners.

Risks Associated with Outsourcing R&D and Rapid Prototyping

The top risks include:

Trade secret theft—unauthorised copying or sharing of designs.
Copyright infringement—misuse of software and design blueprints.
Ownership disputes—who really owns the prototype files and production molds?
Loss of core expertise—outsourcing critical R&D can weaken in-house innovation.
Reputational damage—a security breach can erode investor and customer trust.

International Considerations for Australian Businesses

Varying Legal Frameworks

Australia’s trade secret and IP laws are predominately enforced via civil means, but overseas is another story, especially if you’re outsourcing to less developed countries. Many jurisdictions have weaker protections, making stolen IP difficult to recover or your IP rights difficult to enforce.

Don’t forget – you actually need to have funds available for any legal dispute. If you can’t afford it, then don’t rely on legal battles and contractual enforcement: A good security program is your friend!

Specific Risks for Australian Businesses

Countries with high rates of IP theft pose unique challenges. Contracts mean little if enforcement is lax. This is why due diligence on foreign partners is just as important as the contract itself.

Steps to Protect Your R&D When Outsourcing

Before Outsourcing

Identify and classify critical trade secrets.
Research suppliers’ security track records.
Assess the legal landscape in the outsourcing country.
Perform a security risk assessment to ensure you understand the risks (including supply chain risks and country-specific laws), and what you need to do to manage them.
Develop your Research and Technology Protection Program to ensure you understand the risks and know what controls you need to implement in your contractual measures and operational safeguards

How can Insider Threats manifest in the Supply Chain?

Contractual Measures

Use watertight non-disclosure agreements (NDAs).
Clearly define IP ownership and usage rights in contracts.
Specify dispute resolution mechanisms.
Include post-collaboration IP return/destruction clauses.

Operational Safeguards

Limit access to sensitive data—adopt a need-to-know approach.
Use secure data transfer methods (encrypted channels, VPNs).
Implement strict version control on prototype files.

Monitoring and Control

Conduct regular audits of outsourcing partners.
On-site visits to assess security practices.
Track prototypes through serial numbering and logging systems.
Obtain signed attestations or legally-binding declarations to confirm that all products, materials and designs / data / information have been destroyed or returned on completion of the work.

Legal and Compliance

Maintain detailed documentation of all proprietary designs.
Register copyrights where applicable.
Seek legal counsel in the outsourcing country for enforcement advice.

Conclusion

Innovation thrives on collaboration, but unprotected outsourcing can be a goldmine for IP theft. Trade secrets, fraud, and supply chain risks aren’t hypothetical—they’re real threats with billion-dollar consequences. Protecting your R&D requires a mix of legal safeguards, operational discipline, and continuous oversight.

Want to secure your innovation while staying ahead of the competition? Start by reviewing your outsourcing agreements today—before someone else commercialises your ideas.

Crafting Security Business Cases for Executive Buy-in

Posted on March 25, 2025 by Paul Curwell

Paul Curwell

6–9 minutes

Key Takeaways:

Here’s the bottom line: Executives don’t fund security initiatives; they fund outcomes. A strong business case is essential to get their support.
Focus on Impact, Not Activity: Executives care about how your proposal boosts business outcomes, not your list of security tasks.
Show Value Beyond Compliance: Prove that security investments enable growth, reduce risk, and give your company a competitive edge.
Quantify Risks and Benefits: Use statistics and real-world examples to demonstrate how security measures can save money or prevent significant losses.

What’s the Real Deal with Business Cases for Security?

Let’s be real: writing a business case for security, fraud, or IP protection can feel like trying to convince your dog to do your taxes—it’s tough and often gets ignored. Unlike departments that directly generate revenue, these functions are often viewed as “cost centers.” But the truth is, they’re vital for preventing catastrophic losses. Think about it: how much would a major data breach, insider threat, or IP theft cost your company? Exactly. That’s where your business case comes in.

If you want executives to take your proposal seriously (and fund it), you need more than just a list of security threats or the need for more budget. You need to speak their language. Executives want to know how your proposal will reduce risk, drive growth, and improve profitability. If your business case doesn’t hit those marks, expect a polite nod and zero budget. So how do you get the green light? You need to answer these seven crucial questions in your security business case.

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7 Key questions executives care about – linking security to strategic outcomes

The challenge is proving that security isn’t just about checking boxes or avoiding fines—it’s about tangible business outcomes: protecting revenue, improving customer trust, and enabling expansion into new markets. If you can’t connect security investments to these results, your proposal won’t make it past the trash can. So, let’s dive into the key questions executives are really asking when reviewing your case.

Question 1: What’s the Impact?

Executives want to know how your security investment will improve business resilience, customer trust, or revenue. Security isn’t just about defending against threats; it’s about keeping the lights on, ensuring smooth operations, and even opening new markets. Can your proposal do that? If not, it’s not going to get approved.